For various reasons, a user may have multiple apparatuses that are capable of providing personalized predictive services. For example, the predictive services may be based on learning supervised models that describe the user's behavior given historical data. The learning takes place on the apparatus itself, using data gathered on that apparatus. However, if a user possess or uses multiple such apparatuses, the apparatuses are exposed to different training data, which can cause divergent models. For example, if two apparatuses are given the same input, the two different apparatuses may provide different predictions based on the predictive model of that apparatus.
A typical solution to the divergent model problem is to establish a synchronization infrastructure for making all of the data accessible to all of the apparatuses. However, such a solution typically involves copying the data to a server, which exposes the user to privacy risks and imposes requirements on handling personal data to the server operator.